Economics is in crisis. On one hand, behavioural economics is now well-established, but on the other hand, most economics models are still based on rational expectations with constraints, called “frictions”. The standard program adds more and more constraints to rationality in hopes that this will approximate real behaviour, but this may never work. It is increasingly clear that heterogeneity (the fact that people and institutions are diverse) is essential to understand problems such as inequality. There is a major effort to address this challenge, but the models that do this are technically complicated and rapidly become intractable as they become more realistic. Finally, there is a fundamental challenge due to the fact that we have very little historical data available to fit models for a complicated and evolving economy.
Complexity economics offers solutions to these problems. It advocates modelling behaviour in terms of heuristics and myopic reasoning, as observed in behavioural experiments. It advocates the use of simulations, making it much easier to incorporate heterogeneity in a tractable manner. Finally, it advocates using highly granular data, that accurately captures heterogeneity, to fit the models. Professor Doyne Farmer will present examples where this approach has had success, including applications to technology forecasting, economic growth and climate change, and present a vision of what it can do in the future.
About the speaker
Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, Professor in the Mathematical Institute at the University of Oxford, and an External Professor at the Santa Fe Institute.
His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to the United Bank of Switzerland in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology.
During the eighties he was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. While a graduate student in the 70’s he built the first wearable digital computer, which was successfully used to predict the game of roulette.